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25 changes: 15 additions & 10 deletions README.md
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Expand Up @@ -15,21 +15,17 @@ This is the repo for our [TMLR](https://jmlr.org/tmlr/) survey [Unifying the Per

## News

🔥🔥🔥 [2025/10/11] Featured papers:
🔥🔥🔥 [2025/10/13] Featured papers:

- 🔥🔥 [EvoEngineer: Mastering Automated CUDA Kernel Code Evolution with Large Language Models](https://arxiv.org/abs/2510.03760) from City University of Hong Kong.

- 🔥 [CodeFuse-CR-Bench: A Comprehensiveness-aware Benchmark for End-to-End Code Review Evaluation in Python Projects](https://arxiv.org/abs/2509.14856) from Ant Group.
- 🔥🔥 [LiveOIBench: Can Large Language Models Outperform Human Contestants in Informatics Olympiads?](https://arxiv.org/abs/2510.09595) from University of Michigan.

- 🔥 [BuildBench: Benchmarking LLM Agents on Compiling Real-World Open-Source Software](https://arxiv.org/abs/2509.25248) from Arizona State University.
- 🔥🔥 [Scaling Laws for Code: A More Data-Hungry Regime](https://arxiv.org/abs/2510.08702) from Harbin Institute of Technology.

- 🔥 [Devstral: Fine-tuning Language Models for Coding Agent Applications](https://arxiv.org/abs/2509.25193) from Mistral AI.
- 🔥🔥 [BigCodeArena: Unveiling More Reliable Human Preferences in Code Generation via Execution](https://arxiv.org/abs/2510.08697) from Monash University.

- 🔥 [LLaDA-MoE: A Sparse MoE Diffusion Language Model](https://arxiv.org/abs/2509.24389) from Ant Group.

- 🔥 [Kimi-Dev: Agentless Training as Skill Prior for SWE-Agents](https://arxiv.org/abs/2509.23045) from Moonshot AI.
- 🔥 [CodeFuse-CR-Bench: A Comprehensiveness-aware Benchmark for End-to-End Code Review Evaluation in Python Projects](https://arxiv.org/abs/2509.14856) from Ant Group.

- 🔥 [ML2B: Multi-Lingual ML Benchmark For AutoML](https://arxiv.org/abs/2509.22768) from HSE University.
- 🔥🔥 [EvoEngineer: Mastering Automated CUDA Kernel Code Evolution with Large Language Models](https://arxiv.org/abs/2510.03760) from City University of Hong Kong.

🔥🔥     [2025/08/24] 29 papers from ICML 2025 have been added. Search for the keyword "ICML 2025"!

Expand Down Expand Up @@ -521,6 +517,8 @@ These models are Transformer encoders, decoders, and encoder-decoders pretrained

27. **Mellum**: "Mellum: Production-Grade in-IDE Contextual Code Completion with Multi-File Project Understanding" [2025-10] [[paper](https://arxiv.org/abs/2510.05788)]

28. "Scaling Laws for Code: A More Data-Hungry Regime" [2025-10] [[paper](https://arxiv.org/abs/2510.08702)]

#### Encoder-Decoder

1. **PyMT5** (Span Corruption): "PyMT5: multi-mode translation of natural language and Python code with transformers" [2020-10] [EMNLP 2020] [[paper](https://arxiv.org/abs/2010.03150)]
Expand Down Expand Up @@ -3603,6 +3601,8 @@ For each task, the first column contains non-neural methods (e.g. n-gram, TF-IDF

- "When Names Disappear: Revealing What LLMs Actually Understand About Code" [2025-10] [[paper](https://arxiv.org/abs/2510.03178)]

- "MEC3O: Multi-Expert Consensus for Code Time Complexity Prediction" [2025-10] [[paper](https://arxiv.org/abs/2510.09049)]

### Software Modeling

- "Towards using Few-Shot Prompt Learning for Automating Model Completion" [2022-12] [[paper](https://arxiv.org/abs/2212.03404)]
Expand Down Expand Up @@ -4391,6 +4391,10 @@ For each task, the first column contains non-neural methods (e.g. n-gram, TF-IDF

- "CodeJudgeBench: Benchmarking LLM-as-a-Judge for Coding Tasks" [2025-07] [[paper](https://arxiv.org/abs/2507.10535)]

- "BigCodeArena: Unveiling More Reliable Human Preferences in Code Generation via Execution" [2025-10] [[paper](https://arxiv.org/abs/2510.08697)]

- "How Many Code and Test Cases Are Enough? Evaluating Test Cases Generation from a Binary-Matrix Perspective" [2025-10] [[paper](https://arxiv.org/abs/2510.08720)]

#### Program Synthesis

| Date | Venue | Benchmark | Size | Language | Source |
Expand Down Expand Up @@ -4485,6 +4489,7 @@ For each task, the first column contains non-neural methods (e.g. n-gram, TF-IDF
| 2025-08 | arXiv | FPBench | 1800 | Python | "Refining Critical Thinking in LLM Code Generation: A Faulty Premise-based Evaluation Framework" [[paper](https://arxiv.org/abs/2508.03622)] [[data](https://github.com/JialinLi13/FaultyPremise)] |
| 2025-08 | arXiv | AutoCodeBench | 3,920 | 20 | "AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators" [[paper](https://arxiv.org/abs/2508.09101)] [[data](https://autocodebench.github.io/)] |
| 2025-08 | arXiv | AetherCode | 456 | C++ | "AetherCode: Evaluating LLMs' Ability to Win In Premier Programming Competitions" [[paper](https://arxiv.org/abs/2508.16402)] [[data](https://huggingface.co/datasets/m-a-p/AetherCode)] |
| 2025-10 | arXiv | LiveOIBench | 403 | | "LiveOIBench: Can Large Language Models Outperform Human Contestants in Informatics Olympiads?" [[paper](https://arxiv.org/abs/2510.09595)] [[data](https://liveoibench.github.io/)] |

\* Automatically mined/human-annotated

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